Multiple sensor systems are currently available that provide the ability to simultaneously view hundreds of square miles of surveillance area containing up to thousands of fixed and moving vehicles and assets. The raw sensor returns contain, or can be processed to generate, kinematic estimates (e.g. position, velocity, and bearing) and/or emission profile information. Various automated level-one fusion engines, trackers, have been developed that incrementally assemble the individual sensor reports into tracks, or sequences of radar (or other) reports associated with a single vehicle over time. These trackers provide periodic updates over the kinematic estimates of each battlefield object actively being tracked. Due to these advances in tracking technology the research community now understands well how to extract relevant data about individual physical objects, as described, for example, in Mike Hinman, “Some Computational Approaches for Situation Assessment and Impact Assessment”, Proceedings of the fifth International Conference on Information Fusion, 2002. However, rarely does an individual object work in isolation in the battlefield. Usually groups of objects cooperate to perform a task and achieve a desired goal. Thus, when a higher level understanding of the battlespace is desired (level 2 and 3 fusion tasks as defined by the JDL fusion model (A. Steinberg, C. Bowman, and F. White, “Revisions to the JDL Data Fusion Model”. Joint NATO/IRIS Conference, Quebec, Canada, 1998), the emphasis must shift from the individual object to groups of objects engaged in a common activity. Only when coordinated movements are perceived can the structure, behavior, or intent of an adversary be understood and addressed.
Current target trackers are designed to provide periodic updates of the kinematic state of all the tracks currently being maintained (“live” tracks). The track update rate depends on several factors including the sensor scan rate, the number of live tracks, the available computational resources, etc. The problem of detecting or determining the existence of coordinated action on the part of some of the targets can be defined as the attempt to organize all updated “live” tracks into mutually exclusive track groups and to define their relationships with previously defined groups. At least two factors make this a difficult problem. First, each time a tracker provides “live” track updates there may be, and in general are, a large number of valid track groupings. In particular, for N “live” tracks, the number of different possible groupings of the tracks is 2N−1 (Peter J. Shea, Kathleen Alexander, John Peterson, “Group Tracking using Genetic Algorithms”, Proceedings of the Sixth International Conference on Information Fusion, 2003). Second, each time a new set of mutually exclusive groups is computed, they have to be associated with groupings defined in the previous time instance while maximizing group continuity; in general an NP-hard problem (generally referring to classes of problems that are difficult to solve). More specifically, “The complexity class of decision problems that are intrinsically harder than those that can be solved by a nondeterministic Turin machine in polynomial time; when a decision version of a combinatorial problem is proved to belong to the class of NP-complete problems, then the optimization version is NP-hard.”
Improved andor alternative coordinated-action computer processing apparatus and methods are desired.